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Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI

Mahyar Abbasian, Elahe Khatibi, Iman Azimi, David Oniani, Zahra Shakeri Hossein Abad, Alexander Thieme, Ram Sriram, Zhongqi Yang, Yanshan Wang, Bryant Lin, Olivier Gevaert, Li-Jia Li, Ramesh Jain, Amir M. Rahmani

TL;DR

The paper addresses the lack of foundation metrics for evaluating healthcare chatbots powered by generative AI, arguing that existing intrinsic and extrinsic metrics fail to capture medical semantics, safety, empathy, and personalization. It proposes a user-centered evaluation framework that partitions metrics into four groups—Accuracy, Trustworthiness, Empathy, and Performance—and accounts for confounding variables such as user type, domain, and task. The authors detail specific metrics within each group (e.g., groundedness and up-to-dateness for accuracy; safety, privacy, bias, interpretability for trustworthiness; emotional support and health literacy for empathy; memory efficiency and FLOPs for performance) and outline a framework for benchmarks, human evaluation guidelines, and a leaderboard to enable standardized, end-to-end assessment. They also discuss challenges such as metric associations, evaluation methods, and prompt techniques, and propose future directions including healthcare-specific benchmarks and case studies to validate the framework’s utility in improving patient outcomes and safety in AI-assisted healthcare.

Abstract

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present an comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI

TL;DR

The paper addresses the lack of foundation metrics for evaluating healthcare chatbots powered by generative AI, arguing that existing intrinsic and extrinsic metrics fail to capture medical semantics, safety, empathy, and personalization. It proposes a user-centered evaluation framework that partitions metrics into four groups—Accuracy, Trustworthiness, Empathy, and Performance—and accounts for confounding variables such as user type, domain, and task. The authors detail specific metrics within each group (e.g., groundedness and up-to-dateness for accuracy; safety, privacy, bias, interpretability for trustworthiness; emotional support and health literacy for empathy; memory efficiency and FLOPs for performance) and outline a framework for benchmarks, human evaluation guidelines, and a leaderboard to enable standardized, end-to-end assessment. They also discuss challenges such as metric associations, evaluation methods, and prompt techniques, and propose future directions including healthcare-specific benchmarks and case studies to validate the framework’s utility in improving patient outcomes and safety in AI-assisted healthcare.

Abstract

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present an comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.
Paper Structure (20 sections, 3 figures, 2 tables)

This paper contains 20 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Review of Existing Evaluation Metrics for LLMs
  3. Intrinsic Evaluation Metrics
  4. Extrinsic Evaluation Metrics
  5. Multi-metric Measurements
  6. Essential Metrics for Evaluating Healthcare Chatbots
  7. Accuracy
  8. Trustworthiness
  9. Empathy
  10. Performance
  11. Challenges in Evaluating Healthcare Chatbots
  12. Metrics Association
  13. Evaluation Methods
  14. Model Prompt Techniques and Parameters
  15. Toward an Effective Evaluation Framework
  16. ...and 5 more sections

Figures (3)

  • Figure 1: An overview of the metrics proposed in the literature. The intrinsic metrics are categorized into general LLM metrics and Dialogue metrics, displayed on the left side. On the right side, existing extrinsic metrics for both general domain and healthcare-specific evaluations are presented.
  • Figure 2: A broad overview of the evaluation process and the role of metrics. Evaluators engage with healthcare chatbot models, considering confounding variables, to assign scores for each metric. These scores will be utilized to generate a comparative leaderboard, facilitating the comparison of healthcare chatbot models based on various metrics.
  • Figure 4: An illustrative high-level representation of an evaluation framework containing five main components: models, environment, interface, interacting users, and leaderboard.